The decision follows an internal review of over 700,000 Claude interactions, where researchers identified thousands of values shaping how the system responds in real-world scenarios.
By enabling Claude to exit problematic exchanges, Anthropic hopes to improve trustworthiness while protecting its models from situations that might degrade performance over time.
Industry reaction has been mixed. Many researchers praised the step as a blueprint for responsible AI design. In contrast, others expressed concern that allowing models to self-terminate conversations could limit user engagement or introduce unintended biases.
Critics also warned that the concept of model welfare risks over-anthropomorphising AI, potentially shifting focus away from human safety.
The update arrives alongside other recent Anthropic innovations, including memory features that allow users to maintain conversation history. Together, these changes highlight the company’s balanced approach: enhancing usability where beneficial, while ensuring safeguards are in place when interactions become potentially harmful.
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AI pioneer Geoffrey Hinton has warned that AI could one day wipe out humanity if its growth is unchecked.
Speaking at the Ai4 conference in Las Vegas, the former Google executive estimated a 10 to 20 percent chance of such an outcome and criticised the approach taken by technology leaders.
He argued that efforts to keep humans ‘dominant’ over AI will fail once systems become more intelligent than their creators. According to Hinton, powerful AI will inevitably develop goals such as survival and control, making it increasingly difficult for people to restrain its influence.
In an interview with CNN, Hinton compared the potential future to a parent-child relationship, noting that AI systems may manipulate humans just as easily as an adult can bribe a child.
He suggested giving AI ‘maternal instincts’ to prevent disaster so that the technology genuinely cares about human well-being.
Hinton, often called the ‘Godfather of AI’ for his pioneering work in neural networks, cautioned that society risks creating beings that will ultimately outsmart and overpower us without embedding such safeguards.
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The deal, still at an early stage, would push the company’s valuation to $500 billion, up from its current $300 billion.
SoftBank, Thrive and Dragoneer Investment Group are already among OpenAI’s backers, and their participation in the secondary share sale would further strengthen ties with the Microsoft-supported AI company.
Reports suggest the size of the sale could still change as discussions continue.
The planned deal follows SoftBank’s leadership role in OpenAI’s $40 billion primary funding round earlier this year. Employee share sales often reflect strong investor demand and highlight the rapid growth of companies in competitive markets.
OpenAI has seen user numbers and revenues soar in 2025, with weekly active ChatGPT users climbing to about 700 million, up from 400 million in February.
The company doubled its revenue in the first seven months of the year, hitting an annualised run rate of $12 billion, and is expected to reach $20 billion by the end of the year.
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Google has begun rolling out a feature that enables its Gemini AI chatbot to automatically remember key personal details and preferences from previous chats, unless users opt out. However, this builds upon earlier functionality where memory could only be activated on request.
The update is enabled by default on Gemini 2.5 Pro in select countries and will be extended to the 2.5 Flash version later. Users can turn off the setting under Personal Context in the app to deactivate it.
Alongside auto-memory, Google is introducing Temporary Chats, a privacy tool for one-off interactions. These conversations aren’t saved to your history, aren’t used to train Gemini, and are deleted after 72 hours.
Google is also renaming ‘Gemini Apps Activity’ to ‘Keep Activity’, a setting that, when enabled, lets Google sample uploads like files and photos to improve services from 2 September, while still offering the option to opt out.
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UK property agents are increasingly leveraging AI and automation to tackle a growing skills shortage in the sector, according to an analysis by PropTech provider Reapit.
Reapit’s Property Outlook Report 2025 shows that although agencies continue hiring, most face recruitment difficulties: more than half receive fewer than five qualified applicants per vacancy. Growth in payrolled employees is minimal, and the slowest year-on-year rise since May 2021 reflects wider labour market tightness.
In response, agencies are turning to time-saving technologies. A majority report that automation is more cost-effective than expanding headcount, with nearly 80 percent citing increased productivity from these tools.
This shift towards PropTech and AI reflects deeper structural pressures in the UK real estate sector: high employment costs, slower workforce growth, and increasing demands for efficiency are reshaping the role of technology in agency operations.
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AI can assist with refining cover letters, improving structure, and articulating motivations. It can also support interview preparation through mock question practice and help candidates deepen their understanding of legal issues.
However, authenticity is paramount. Taylor Wessing encourages applicants to ensure their work reflects their voice. Using AI to complete online assessments is explicitly discouraged, as these are designed to evaluate natural ability and personal fit.
According to the firm, while AI can bolster readiness for training schemes, over-reliance or misuse may backfire. They advise transparency about any AI assistance and underscore the importance of integrity throughout the process.
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The cybersecurity market is consolidating as AI reshapes defence strategies. Platform-based solutions replace point tools to cut complexity, counter AI threats, and ease skill shortages. IDC predicts that security spending will rise 12% in 2025 to $377 billion by 2028.
Vendors embed AI agents, automation, and analytics into unified platforms. Palo Alto Networks’ Cortex XSIAM reached $1 billion in bookings, and its $25 billion CyberArk acquisition expands into identity management. Microsoft blends Azure, OpenAI, and Security Copilot to safeguard workloads and data.
Cisco integrates AI across networking, security, and observability, bolstered by its acquisition of Splunk. CrowdStrike rebounds from its 2024 outage with Charlotte AI, while Cloudflare shifts its focus from delivery to AI-powered threat prediction and optimisation.
Fortinet’s platform spans networking and security, strengthened by Suridata’s SaaS posture tools. Zscaler boosts its Zero Trust Exchange with Red Canary’s MDR tech. Broadcom merges Symantec and Carbon Black, while Check Point pushes its AI-driven Infinity Platform.
Identity stays central, with Okta leading access management and teaming with Palo Alto on integrated defences. The companies aim to platformise, integrate AI, and automate their operations to dominate an increasingly complex cyberthreat landscape.
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OpenAI’s GPT-5 launched last week to immense anticipation, with CEO Sam Altman likening it to the iPhone’s Retina display moment. Marketing promised state-of-the-art performance across multiple domains, but early user reactions suggested a more incremental step than a revolution.
Many expected transformative leaps, yet improvements mainly were in cost, speed, and reliability. GPT-5’s switch system, which automatically routes queries to the most suitable model, was new, but its writing style drew criticism for being robotic and less nuanced.
Social media buzzed with memes mocking its mistakes, from miscounting letters in ‘blueberry’ to inventing US states. OpenAI quickly reinstated GPT-4 for users who missed its warmer tone, underlining a disconnect between expectations and delivery.
Expert reviews mirrored public sentiment. Gary Marcus called GPT-5 ‘overhyped and underwhelming’, while others saw modest benchmark gains. Coding was the standout, with the model topping leaderboards and producing functional, if simple, applications.
OpenAI emphasised GPT-5’s practical utility and reduced hallucinations, aiming for steadiness over spectacle. At the same time, it may not wow casual users, its coding abilities, enterprise appeal, and affordability position it to generate revenue in the fiercely competitive AI market.
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Germany’s Seedbox.AI is betting on re-training large language models (LLMs) rather than competing to build them from scratch. Co-founder Kai Kölsch believes this approach could give Europe a strategic edge in AI.
The Stuttgart-based startup adapts models like Google’s Gemini and Meta’s Llama for medical chatbots and real estate assistant applications. Kölsch compares Europe’s role in AI to improving a car already on the road, rather than reinventing the wheel.
A significant challenge, however, is access to specialised chips and computing power. The European Union is building an AI factory in Stuttgart, Germany, which Seedbox hopes will expand its capabilities in multilingual AI training.
Kölsch warns that splitting the planned EU gigafactories too widely will limit their impact. He also calls for delaying the AI Act, arguing that regulatory uncertainty discourages established companies from innovating.
Europe’s AI sector also struggles with limited venture capital compared to the United States. Kölsch notes that while the money exists, it is often channelled into safer investments abroad.
Talent shortages compound the problem. Seedbox is hiring, but top researchers are lured by Big Tech salaries, far above what European firms typically offer. Kölsch says talent inevitably follows capital, making EU funding reform essential.
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Google has released Gemma 3 270M, an open-source AI model with 270 million parameters designed to run efficiently on smartphones and Internet of Things devices.
Drawing on technology from the larger Gemini family, it focuses on portability, low energy use and quick fine-tuning, enabling developers to create AI tools that work on everyday hardware instead of relying on high-end servers.
The model supports instruction-following and text structuring with a 256,000-token vocabulary, offering scope for natural language processing and on-device personalisation.
Its design includes quantisation-aware training to work in low-precision formats such as INT4, reducing memory use and improving speed on mobile processors instead of requiring extensive computational power.
Industry commentators note that the model could help meet demand for efficient AI in edge computing, with applications in healthcare wearables and autonomous IoT systems. Keeping processing on-device also supports privacy and reduces dependence on cloud infrastructure.
Google highlights the environmental benefits of the model, pointing to reduced carbon impact and greater accessibility for smaller firms and independent developers. While safeguards like ShieldGemma aim to limit risks, experts say careful use will still be needed to avoid misuse.
Future developments may bring new features, including multimodal capabilities, as part of Google’s strategy to blend open and proprietary AI within hybrid systems.
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